Details

Autor(en) / Beteiligte

• Li, Dayu
• Li, Yang
• Wang, Suge

Titel

Interactive double states emotion cell model for textual dialogue emotion prediction

Ist Teil von

• Knowledge-based systems, 2020-02, Vol.189, p.105084, Article 105084

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2020

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• Daily dialogues are full of emotions that control the trends of dialogues and influence the attitudes of interlocutors toward each other, and understanding the human emotions in dialogues is of great significance in emotional comfort, human–computer interaction and intelligent question-answering. This paper defines a new task called emotion prediction in textual dialogue. Different from the text emotion recognition task, which derives the current emotional state of interlocutor from the utterance, emotion prediction aims at predicting the future emotional state of interlocutor before the interlocutor utters something. Moreover, this paper summarizes and explains three notable characteristics of emotional propagation in text dialogue: context dependence, persistence and contagiousness. By considering these characteristics, a fully data-driven interactive double states emotion cell model (IDS-ECM) is proposed. The model has two layers. The first layer automatically extracts the emotional information of historical dialogue and is used to describe the contextual dependence of the textual dialogue emotion. The second layer models the change process of interlocutors’ emotional states during the dialogue and depicts the persistence and contagiousness of emotions. Experimental results on two manually annotated datasets show that the proposed model is superior to the baseline in the macro-averaged F1 evaluation metric and that the proposed model can simulate the emotional changes in the process of dialogue so as to predict the emotions with high accuracy. The experimental results also reveal the communication differences between different emotional categories in dialogue, which is of guiding significance for future research.